IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v475y2023ics0304380022003015.html
   My bibliography  Save this article

Chaos does not drive lower synchrony for intrinsically-induced population fluctuations

Author

Listed:
  • Grosklos, Guenchik
  • Zhao, Jia

Abstract

Amphibians naturally occur in metapopulations characterized by spatially separated breeding habitats connected by dispersing individuals. The rate at which individuals grow to maturity, size of the metapopulation, and movement behavior varies widely across amphibian species, and their compounding interactions play a large role in population dynamics and viability. When populations in a connected network exhibit cyclic behavior the level of synchrony between populations is important for assessing extinction risk. In addition, the qualitative behavior of fluctuations provides insight into the patterns of the population cycles and can be used to predict forward trajectories in time. Chaotic oscillations, characterized by aperiodic cycles and sensitivity to initial conditions, are known to amplify noise, thus lowering population synchrony; however, other oscillation types (invariant cycles, k-cycles) have not been explicitly explored in relation to synchrony. In this paper, we investigate the relationship between synchrony and oscillation type for a two-patch system of a species with 1, 2, and 3 life-history stages. Using dynamical systems analysis, we determine the mechanisms that induce the different oscillation types and relate them with dispersal rates and synchrony. We find that dispersal has a greater effect on population dynamics of a species with 1 life-history stage compared to the subtle changes in dynamics found for species with 2 and 3 life-history stages. For low levels of dispersal, oscillating populations are driven to equilibrium as synchrony increases. Under medium to high levels of dispersal, oscillations may be created from equilibrium with low levels of synchrony. In general, chaos does not have noticeably lower synchrony than other oscillation types but has synchrony levels comparable to the oscillation types surrounding chaos. In this study, we cover a broad range of dispersal probabilities and life histories intended for general amphibian systems. The variety of results found in our analysis emphasizes the importance of determining model parameters and life history assumptions when studying specific amphibian species to ensure that the resulting dynamics accurately reflect the system.

Suggested Citation

  • Grosklos, Guenchik & Zhao, Jia, 2023. "Chaos does not drive lower synchrony for intrinsically-induced population fluctuations," Ecological Modelling, Elsevier, vol. 475(C).
    • Handle: RePEc:eee:ecomod:v:475:y:2023:i:c:s0304380022003015
    DOI: 10.1016/j.ecolmodel.2022.110203
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380022003015
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2022.110203?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Matthew D. Holland & Alan Hastings, 2008. "Strong effect of dispersal network structure on ecological dynamics," Nature, Nature, vol. 456(7223), pages 792-794, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dongli, Duan & Chengxing, Wu & Yuchen, Zhai & Changchun, Lv & Ning, Wang, 2022. "Coexistence mechanism of alien species and local ecosystem based on network dimensionality reduction method," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    2. Liao, Limei & Shen, Yang & Liao, Jinbao, 2020. "Robustness of dispersal network structure to patch loss," Ecological Modelling, Elsevier, vol. 424(C).
    3. Wang, Jin-Liang & Wu, Huai-Ning, 2011. "Stability analysis of impulsive parabolic complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 44(11), pages 1020-1034.
    4. Shen, Yang & Zeng, Chenghui & Nijs, Ivan & Liao, Jinbao, 2019. "Species persistence in spatially regular networks," Ecological Modelling, Elsevier, vol. 406(C), pages 1-6.
    5. Borrett, S.R. & Freeze, M.A., 2011. "Reconnecting environs to their environment," Ecological Modelling, Elsevier, vol. 222(14), pages 2393-2403.
    6. Salau, Kehinde & Schoon, Michael L. & Baggio, Jacopo A. & Janssen, Marco A., 2012. "Varying effects of connectivity and dispersal on interacting species dynamics," Ecological Modelling, Elsevier, vol. 242(C), pages 81-91.
    7. Henriette Heer & Lucas Streib & Ralf B Schäfer & Stefan Ruzika, 2020. "Maximising the clustering coefficient of networks and the effects on habitat network robustness," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-16, October.
    8. Borrett, Stuart R. & Moody, James & Edelmann, Achim, 2014. "The rise of Network Ecology: Maps of the topic diversity and scientific collaboration," Ecological Modelling, Elsevier, vol. 293(C), pages 111-127.
    9. Bagchi, Dweepabiswa & Arumugam, Ramesh & Chandrasekar, V.K. & Senthilkumar, D.V., 2022. "Metacommunity stability and persistence for predation turnoff in selective patches," Ecological Modelling, Elsevier, vol. 470(C).
    10. Schreiber, Sebastian J. & Killingback, Timothy P., 2013. "Spatial heterogeneity promotes coexistence of rock–paper–scissors metacommunities," Theoretical Population Biology, Elsevier, vol. 86(C), pages 1-11.
    11. Borrett, S.R. & Salas, A.K., 2010. "Evidence for resource homogenization in 50 trophic ecosystem networks," Ecological Modelling, Elsevier, vol. 221(13), pages 1710-1716.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:475:y:2023:i:c:s0304380022003015. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.